bio-machine-learning-biomarker-discovery
Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization. Use when identifying biomarkers from high-dimensional omics data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill biomarker-discovery --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: numpy 1.26+, pandas 2.2+, scikit-learn 1.4+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Feature Selection for Biomarker Discovery **"Find the best biomarkers in my omics data"** → Select informative features using all-relevant selection (Boruta), minimum redundancy (mRMR), or regularization (LASSO) to identify candidate biomarkers. - Python: `BorutaPy(rf, n_estimators='auto')`, `sklearn.linear_model.LassoCV()` ## Boruta All-Relevant Selection Identifies all features that are significantly better than random (shadow features). ```python from boruta import BorutaPy from sklearn.ensemble import RandomForestClassifier import pandas as pd import numpy as np rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, random_state=42) # max_iter=100: Typically sufficient; increase to 200 if many features remain tentative # perc=100: Use max of shadow features (de
- Version Compatibility
- Boruta All-Relevant Selection
- mRMR (Minimum Redundancy Maximum Relevance)
- LASSO Feature Selection
- Univariate Filtering (Pre-filter)
- Combined Pipeline
- Method Comparison
- Stability Selection
- Related Skills
What does the bio-machine-learning-biomarker-discovery skill do?
Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization. Use when identifying biomarkers from high-dimensional omics data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill biomarker-discovery --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.